Probabilistic Graphical Models for Semi-Supervised Traffic Classification

by Charalampos Rotsos, Jurgen Van Gael, Andrew W Moore, Zoubin Ghahramani
The 6th International Wireless Communications and Mobile Computing Conference ()


Traffic classification using machine learning\ncontinues to be an active research area. The\nmajority of work in this area uses off-the-shelf\nmachine learning tools and treats them as black-box\nclassifiers. This approach turns all the modelling\ncomplexity into a feature selection problem. In this\npaper, we build a problem-specific solution to the\ntraffic classification problem by designing a custom\nprobabilistic graphical model. Graphical models are\na modular framework to design classifiers which\nincorporate domain-specific knowledge. More\nspecifically, our solution introduces\nsemi-supervised learning which means we learn from\nboth labelled and unlabelled traffic flows. We show\nthat our solution performs competitively compared to\nprevious approaches while using less data and\nsimpler features.

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